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1 https://wordpress.org/?v=5.1.1The Knowledge Translation Complexity Network (KTCN) modelhttps://realkm.com/2019/03/22/the-knowledge-translation-complexity-network-ktcn-model/
https://realkm.com/2019/03/22/the-knowledge-translation-complexity-network-ktcn-model/#respondThu, 21 Mar 2019 14:45:41 +0000https://realkm.com/?p=14723Multiple models for the translation of evidence into healthcare policy and practice have been articulated. However, most are linear or cyclical, and very few come close to reflecting the dense and intricate relationships, systems and politics of organizations, or the processes required to enact sustainable improvements.

A recent paper1 illustrates how using complexity and network concepts can better inform knowledge translation. The ideas presented in the paper have been developed and refined by a cross-faculty interdisciplinary team in the Faculty of Health and Medical Sciences at the University of Adelaide, Australia. They argue that changing the way we think and talk about knowledge translation could enhance the creation and movement of knowledge throughout systems that need to develop and utilise that knowledge.

As shown in the diagram above, the paper proposes that knowledge translation is a complex network composed of five interdependent sub-networks, or clusters, of key processes (problem identification [PI], knowledge creation [KC], knowledge synthesis [KS], implementation [I], and evaluation [E]) that interact dynamically in different ways at different times across one or more sectors (for example community, health, government, education, research).

This is called the Knowledge Translation Complexity Network (KTCN), defined as a network that optimises the effective, appropriate and timely creation and movement of knowledge to those who need it in order to improve what they do.

The emerging KTCN model emphasises the central importance of individual actors within networks and who interact with others, thus forming clusters of activity. The paper argues that this is more likely to be achieved when knowledge transfer acknowledges a network of Complex Adaptive Systems.

Complex Adaptive Systems encompass ideas, concepts and tools that can be applied across multiple disciplines. They demonstrate the property of emergence; where macro-level properties arise from the interactions of lower level activities. They are robust, and within them, cumulative small occurrences have the ability to suddenly pass a critical threshold and produce large events.

How knowledge is created and mobilised within social Complex Adaptive Systems is determined by the relationships and shared understandings of what the benefits and incentives are for the movement of that knowledge. Understanding of such benefits may be explicit (as in the form of a set of objectives, mission statement or goals) but more often they are implicit, reflecting the common consciousness or prevailing motives, values and relationships of a group of colleagues, a team or a network who work together to create a common goal.

As shown in the KTCN model diagram above, the boundaries around clusters are dynamic: they can adapt and adopt according to the activities within and between the clusters. The KTCN is not simply defined by the sum of its parts . Relationship between these parts is of paramount importance. The main qualities of the KTCN is that it is a Complex Adaptive System comprised of its agents and characterised by interactions, self-organization, non-linearity, dynamics, emergence and co-evolution as defined in Figure 1.

In a follow-on paper2, the team has responded to a range of academic commentary on the original paper.

The next task will be to work collaboratively with stakeholders to generate the guiding principles or simple rules that normally reflect Complex Adaptive Systems. Such guiding principles will enable more widespread uptake and use of these ideas and facilitate others in applying the KTCN model.

]]>https://realkm.com/2019/03/22/the-knowledge-translation-complexity-network-ktcn-model/feed/0Is a new conceptual model really the roadmap for BIM-based KM in construction projects?https://realkm.com/2019/03/21/is-a-new-conceptual-model-really-the-roadmap-for-bim-based-km-in-construction-projects/
https://realkm.com/2019/03/21/is-a-new-conceptual-model-really-the-roadmap-for-bim-based-km-in-construction-projects/#respondThu, 21 Mar 2019 11:08:50 +0000https://realkm.com/?p=14707This article is part of an ongoing series looking at knowledge management (KM) in the building and construction industries.

A new conference paper1 is similarly conceptual, with the authors carrying out a comprehensive literature review on KM and BIM to construct the theoretical framework for a conceptual model for BIM-based KM in construction projects. Although conceptual, this new study claims to significantly advance BIM-based KM, with the authors stating that “The proposed conceptual model is a road map for adapting KM process with BIM as a new KM tool.”

Sadly, however, the conceptual model fails to adequately deliver on this promise.

The model describes a series of steps leading to the establishment of an ‘electronic-intelligent building knowledge model’ (e-iBKM). This “provides knowledge in a cloud-based environment,” with the paper authors arguing that current and future developments in information and communications technology (ICT) will bring about a new era in BIM-based KM.

However the new KM standard ISO 30401:2018 Knowledge management systems – Requirements advises that “knowledge is intangible and complex; it is created by people,” and alerts that there are “many common misconceptions about how to do knowledge management, for example the view that simply buying a technology system will be enough for knowledge management.”

While the proposed e-iBKM will potentially assist BIM-based KM, a technology system such as this is just one of the knowledge management enablers needed in a knowledge management system. The other enablers include human capital, organisational processes and governance, and a knowledge management culture.

To ensure the appropriate development of BIM-based KM, the construction industry is encouraged to consider and apply the new KM standard ISO 30401:2018 Knowledge management systems – Requirements and to also seek advice from KM practitioners and networks. Similarly, KM practitioners and organisations can pursue the establishment of beneficial links with construction industry organisations.

]]>https://realkm.com/2019/03/21/is-a-new-conceptual-model-really-the-roadmap-for-bim-based-km-in-construction-projects/feed/0Companies are failing to get value from innovationhttps://realkm.com/2019/03/19/companies-are-failing-to-get-value-from-innovation/
https://realkm.com/2019/03/19/companies-are-failing-to-get-value-from-innovation/#respondTue, 19 Mar 2019 02:09:07 +0000https://realkm.com/?p=14697Originally posted on The Horizons Tracker.

Innovation is something that everyone says they want to do, but it seems increasingly clear that this desire is often rather superficial. For instance, recently I wrote about a new study from Harvard Business School showing that innovation is rarely a top priority for executives. Indeed, just 30% placed it in their top 3 issues to focus on in the coming years.

A new study1 from Accenture may shed some light into this apparent lack of enthusiasm at board level. It reveals that just 14% of companies have managed to secure the return on their innovation investment that they had originally hoped for.

The report reveals that companies have spent around £2.5tn on innovation in the last five years, but have very little to show for it. I’ve mentioned previously that the ROI of innovation, in patent terms at least, is getting worse as R&D expenditure has boomed in recent years to produce the same output as more meagre investments earned in the past.

Research2 from the Stanford Institute for Economic Policy Research found that truly novel ideas are not only harder and harder to come by, but they tend to be ever more expensive to explore.

The analysis found that whilst research spending is going up considerably (around 200 billion Euros in the EU alone), the ideas output by each researcher is going down considerably. They suggest that this huge increase in research inputs has helped the American economy to maintain growth as this increase has offset the decline in productivity. This is reflected in the number of people engaged in R&D, which has mushroomed twentyfold since 1930.

Getting a return on innovation

Accenture suggest that the amount one spends is nowhere near as important as how you spend it. They believe that the companies getting the best returns have invested in breakthrough innovation rather than aiming for the more incremental sort.

“Fortune favours the bold when it comes to investing in innovation. The companies reaping the biggest rewards show a “go big or go home” mentality by investing in truly disruptive innovation projects. They don’t just tinker around the edges,” Accenture say.

The report goes on to suggest a number of characteristics that they believe distinguishes the best and most innovative companies from their peers. They suggest that to be truly innovative, companies should be:

Tackling only the problems that are most important to customers

Harnessing the power of ‘the crowd’ to tap into breakthrough knowledge

Tapping into the best talent, both from inside and outside the business

Ensuring that your work is data-driven, so that it generates, shares and applies data in the application of new innovations

Mastering the latest technologies to power innovation

Including a broad range of stakeholders to tackle the needs both of customers and wider society.

These all seem pretty obvious and straightforward tips, yet Accenture believe even these are rarely being seen in practice, which has contributed to a 27% decline in innovation return on investment in recent years.

“The fact that return on investment overall is dropping is a worrying trend. Business are spending more than ever, but their inability to see proper returns is shocking. One of the reasons for this could be that many organisations still see innovation as a peripheral activity separate to the core business; an “ad-hoc creative process” rather than a set of practices that will fundamentally change their way of doing business,” Accenture say.

Hard science

With around a third of respondents to the Accenture survey planning to increase their spend on innovation in the coming years, it is undoubtedly true that companies need to get better at it. What perhaps the Accenture report fails to address however is that making truly breakthrough innovations requires a level of science that is increasingly hard to explore.

The importance of hard science was highlighted in a recent study3 that highlighted the clear connection between pure scientific research and patentable inventions. The research looked at any connections that exist between every single patent issued between 1976 and 2015 by the US Patent and Trademark Office (of which there were around 4.8 million), and every single journal article published since WW2 (around 32 million).

They found a clear and constant flow between pure science and practical innovations. Whilst there are, of course, some papers that are rarely cited by future work, of those with at least one citation, a whopping 80% contributed to a future patent. Similarly, 61% of all patents referenced a research paper.

Whilst there are no shortage of ‘recipe books’ aiming to guide leaders towards innovation success, the evidence underlines just how difficult it is. If innovation is to retain the attention of executives however, it’s vital that innovation professionals manage to up their game and provide a clearer path to returns than is currently the case.

]]>https://realkm.com/2019/03/19/companies-are-failing-to-get-value-from-innovation/feed/0Exploring the science of complexity series (part 16): Concept 7 – Strange attractors and the ‘edge of chaos’https://realkm.com/2019/03/18/exploring-the-science-of-complexity-series-part-16-concept-7-strange-attractors-and-the-edge-of-chaos/
https://realkm.com/2019/03/18/exploring-the-science-of-complexity-series-part-16-concept-7-strange-attractors-and-the-edge-of-chaos/#respondMon, 18 Mar 2019 00:38:49 +0000https://realkm.com/?p=14683This article is part 16 of a series of articles featuring the ODI Working Paper Exploring the science of complexity: Ideas and implications for development and humanitarian efforts.

Complexity and systems – Concepts 4, 5, 6, and 7

The next four concepts relate to different aspects of how complex systems – those characterised by Concepts 1-3 – change over time. The causal relationships that play out within complex systems are explained using the concept of nonlinearity (Concept 4) and the sensitivity of complex systems to their starting conditions is highlighted (Concept 5). The overall shape of the system and its future possibilities are described using the idea of phase space (Concept 6). The patterns underlying seeming chaos within complex systems are explained (Concept 7).

Concept 7 – Strange attractors and the ‘edge of chaos’

Outline of the concept

The concept of phase space and attractors are central to understanding complexity, as complexity relates to specific kinds of system trajectories through phase space over time. The behaviour of complex systems can at first glance appear to be highly disordered or random. Moreover, these systems move through continually new states, with change as a constant in a kind of unending turbulence. However, there is an underlying pattern of order that is recognisable when the phase space of the system is mapped, known as a strange attractor.

Detailed explanation

In the 19th century, a mathematician named Henri Poincare was using Newton’s equations of planetary motion, which were – as has already been covered – based on a number of assumptions of linearity. Poincare proved that this approach worked for simple planetary systems of two bodies. In order to test the applicability with systems of three bodies – e.g. the sun and two planets – Poincare used tools that were based on the same principles as phase space to map the movement of such systems over time. He found the trajectory of the system to be one of ‘awesome complexity’ 1. The idea lay more or less dormant until the 1960s, when the Lorenz experiments showed that computer-aided modelling could be used to identify complexity.

Until the 1960s, there were only a few known attractors – including fixed points and periodic (as described previously in concept 6). All of these attractors related to systems that are predictable, in terms of understanding where they may end up. However, complex systems that are hard to predict also have an attractor, but they are much harder to map without the use of computers. The attractor for complex systems was discovered by Lorenz (shown in Figure 1). Most commonly known as strange attractors2, these are at the heart of the understanding of complexity.

Strange attractors show how complex systems move around in phase space, in shapes which resembles two butterfly wings3. A complex system – such as the three-body planetary system, or the weather – would move around one loop of the attractor, spiralling out from the centre. When it got close to the edge of the ‘wing’ it would move over to the other ‘wing’ and spiral around again4. Complex systems can have a chaotic dynamic, and develop through a series of sudden jumps5. Such a jump, usually referred to as a bifurcation, is an abrupt change in the longterm behaviour of a system, when the value of a particular dimension becomes higher or lower than some critical value. As one gets close to the bifurcation points – which may be seen as those points where the system moves from one wing of the attractor to the other, the values of fluctuations increase dramatically.

This strange attractor shows that complexity – although seemingly completely disordered, actually displays order at the level of its trajectory, and that although it may be unpredictable in its detail, it always moves around the same attractor shape. This ‘narrowness of repertoire’ is at the heart of the order hidden in complexity.

The lines in the attractor reflect the overall pattern of system behaviour, rather than the sequential movement of the system through time. Points on this attractor appear haphazardly at various locations on the lines, over time, eventually revealing the lines, but giving the observer no clue as to where the point will next appear within phase space. Eventually, the overall pattern of system behaviour is revealed. As Sanders puts it:

There are systems that never settle into a predictable or steady state … these are said to have strange attractors. A graphic representation of such a system will reveal a complicated pattern or shape, where the internal design never repeats itself 78

These observations offer an explanation of why elaborate computer programs cannot predict weather patterns with 100% accuracy. Yet, although the weather is unpredictable, it remains bounded within a certain ‘space of the possible’. A complex system is thus dynamic and nonlinear, and it is hard to predict the outcome of a given input and the feedback loops this causes. When the feedback is positive there is progression: the system moves forward. Feedback loops do not always produce the same effects and are not predictable. Paradoxically, complex feedback systems act to control the chaos in complex systems and keep them within certain boundaries9. A somewhat poetic view of a city from this perspective illustrates this:

Buyers, sellers, administrations, streets … are always changing, so that a city’s coherence is somehow imposed on a perpetual flux of people and structures. Like the standing wave in front of a rock in a fast-moving stream, a city is a pattern in time. No constituent remains in place but the city persists10

Such systems do display order, albeit not in the regular sense expected with linear systems. Instead, the order relates to the shape or pattern that the behaviour of a system displays in its phase space over time.

At a more general level, the notion of strange attractors and bifurcations implies that, despite chaotic or turbulent behaviour, the dynamics of complex systems can be investigated and understood. With the use of these tools, complexity scientists have been able to shed light on situations where there is no settling down to a stable equilibrium, no stable states and no repetition. Instead, there are systems undergoing continuous change, driven by the various factors and actors that shape and make them. This process of continuous change is often referred to as far from equilibrium, or ‘unending turbulence’.

This resonates with much thinking in political science, which suggests that ‘economic innovation [is] often driven by social conflicts within economic systems [and] seems to be a constant generator of fluctuations in capitalist social systems’ 11. In fact, it is possible that a very large number of phenomena in the physical and social worlds can be better understood as complex systems undergoing continuous change and operating far from equilibrium. To cite one thinker [Jake Chapman], ‘our society and all of its institutions are in continuing processes of transformation … we must learn to understand, guide, influence and manage these transformations’ 12. For some, this means operating at the ‘edge of chaos’. A previous ODI working paper looks at applying complexity theory to the process of strengthening capacity in community-based natural resource management organisations 13. Using examples of organisations from the Fiji Islands, Papua, West Bengal and Venezuela, Warner examines three approaches to the adaptation of CBNRM and investigates how organisations can be assisted to manage and adapt in the face of these increasing development pressures. Warner argues that, within certain limits,

… methods of interest-based negotiation can be applied to solicit organisation-specific rules that draw … organisations away from development-induced conflict and social exclusion towards an “edge of chaos” where creativity and adaptation flourish14

Box 1 provides a more detailed look at the concept of ‘edge of chaos’, with specific reference to urban planning and urban regeneration.

Box 1: Edge of chaos

Attractors suggest that systems are understood in terms of the two extremes of order and chaos. The metaphor of solids and gases can be used to clarify this. In solids, atoms are locked into place, whereas in gases they tumble over one another at random. However, right in between the two extremes, at a phase transition, a phenomenon called the ‘edge of chaos’ occurs. This phenomenon describes systems behaviours where the components of the system never quite lock into place and never quite dissolve into turbulence either. In human organisations, the simplest example is of a system that is neither too centrally controlled (order) nor too unorganised (chaos). The key question for many thinkers, who suggest that the edge of chaos is the place of maximum innovation within human systems, is how complex systems get to the edge of chaos. The illustration above on solids and liquids suggests, logically, that ordered systems can achieve this by loosening up a bit, and chaotic ones can do it by getting themselves a little more organised.

A study of urban planning has suggested that the edge of chaos principle relates to the evolving relationship between local authorities and local communities in Hulme, Manchester. This shaped decision-making processes, steering a path between the two extremes of centralised order (local authorities) and bottom-up chaos (community groups). Using social network analysis, the diminishing gap between authorities and communities was measured, drawing conclusions about the strength of the ties and frequency of interaction between the two groups over time. It was found that, from 1960–85, decision making was enshrined in the notion of local authorities making decisions for local communities without the latter being consulted. By the mid-1980s, consensus was beginning to loosen up under the exigencies of the (emergent) local community networks, moving from the highly centralised ‘we know best’ spirit of the 1960s to an acceptance of the opinions of community groups as useful and valuable in the decision-making process. This represented a massive change and paved the way for real progress in Hulme in the 1990s. Hulme as a system was searching for the edge of chaos, a special kind of balance (in decision making) between central control and the power of community networks. An important point to note here is that nobody designed the search process for the consensus that ensued – the system itself found the balance. A programme was then launched which was able to flourish on this fertile ground of strengthened community–authority interactions. Subsequent evaluations on the regeneration processes highlighted that the success of the initiative came about because it was at a particularly innovative point for community–authority relations. This highlights a potentially fundamental insight into the understanding of the urban system in general and urban regeneration processes in particular.

If social systems cannot best be described by reference to fixed point or periodic attractors, this means that social phenomena should not be viewed as tending towards equilibrium, as having defined endstates, or as being cyclical. A more apt metaphor, and one which may help to further understanding, may be to view them as open systems that exchange energy, matter or information with each other and their environment, and that continually create new structures and order 16. Nobel Prize winner Ilya Prigogine has referred to these as ‘dissipative structures’, in reference to his research on a wide range of systems that displayed such behaviours. Such systems can maintain themselves in stable states which are far from equilibrium, and can transform themselves into new structures of increased complexity. Prigogine’s analysis details how instabilities and bifurcations to new structures are in fact the result of fluctuations which are amplified by positive feedback processes.

Example: Organisational change as bifurcations in a complex system

Gareth Morgan17 suggests that the idea of strange attractors provides a powerful perspective for the management of stability and change in organisations. Specifically, he suggests that transformational change ultimately involves the creation of new contexts that can break the hold of dominant attractor trajectories in favour of new ones. He uses the idea of a strange attractor as a creative metaphor (as shown in Figure 2) to generate thinking about organisational change, and in doing so raises an important challenge for managers of change processes. If organisations can be described using the attractor metaphor, then it is implied that managers cannot be in control of the change. The new pattern of the attractor cannot be precisely defined – it is only possible to nurture elements of the new context, and create conditions under which the new context can arise. When the old pattern – the old context – is particularly powerful, no significant change is possible, because the organisation ends up trying to do new things in old ways. Morgan sees that the power of this approach lies in its potential both to open up new understandings and possibilities for action but also, importantly, to outline the limitations in terms of individual actors’ control and power over organisational change processes.

Implication: Manage contexts and ensure decision-making approaches are appropriate to the system

Certain social, economic and political domains seem – at least metaphorically – to fit the image of the strange attractor metaphor, with discontinuities, perpetual novelty and ever-changing elements but recurring patterns and discernible structures. This is true of international aid – as Porter et al.19 argue, the whole international development system is ‘a moving, evolving multi-faceted thing, and if it was possible to offer an answer today, it would be inappropriate by tomorrow.’

This equally applies to the process of development in a country, or the adequate means of responding to crises. A particular issue in international aid may not be most usefully solved through the provision of a particular ‘output’. Rather, it may be more productive to see development as an open-ended, ongoing, unpredictable and continually changing process. Similarly, crises can be seen as bifurcation points in which human social systems are exposed to high constraints and stress ‘that upset the balance between the internal forces structuring the systems and the external forces that make up the environment’ 20. A crisis could then be defined as a condition in which there is a change in an environmental or human stress that is destabilising enough so that the original set of attractors is supplanted by a new set of attractors.

However, this perspective contrasts with attitudes towards chance and risk prevalent within aid organisations:

Venturing into the unknown normally means that the organisation’s standard operating procedures can no longer deal with the types of information it is receiving, and are no longer suitable. Such departures occur when the organisation is on the brink of collapse or is being forced – by means no longer in its control – to change its procedures fundamentally. It often takes a long time for an [aid] organisation to realise that it has hit the point where there is no alternative to change; often, that point comes too late21

Accepting the notion of chaos and strange attractors encourages an acknowledgement of the continual change in social systems, which by extension requires acceptance of ‘the inevitability of change’ in the many systems that aid agencies operate within and around. Such change should not be viewed as worrying or necessarily negative22. Equally, equilibrium and stability should not be viewed as default and ideal states for a particular system, but as situations of stasis and ossification. Incorporating this insight into the way problems are approached in the development sector points towards an important shift in thinking. As Peter Senge suggests:

… most of us have been conditioned throughout our lives to focus on things and to see the world in static images. This [in turn] leads to linear explanations of “systemic phenomenon”. Understanding the perpetual flux in systems should lead us to see “interrelationships, not things, and processes, not snapshots”…23

Whether looking within aid organisations or outside them, this calls for better management of context, and to give up the idea of precise control in favour of the idea of the emergent nature of change. New contexts can be generated through new understandings, such that those operating in the system can be encouraged to challenge and change existing paradigms, norms and assumptions. For example, thinking of an organisation as discrete may lead those within it to try and help it survive as a discrete entity, instead of allowing it to evolve to a new form. New contexts can also be encouraged by identifying and changing the ‘basic rules’ which reinforce the existing attractor patterns, allowing new actions to emerge and become powerful messages for the kind of change that is being sought. These changes can help to catalyse other changes that are in line with the hoped-for new context24.

The potential for small changes to lead to large, directed changes is explored by Holland in his work on ‘lever points’ of systems. Holland25 argues that such lever points could be the key to solving problems such as ‘immune diseases, inner city decay, industrial innovation, and the like’. The chaos metaphor suggests that there are bifurcation points that tip systems from one state to another – and, if these can be understood, then it may be possible to better identify such leverage points. This has been explored in the context of aid effectiveness26 by investigation of the premise that a relatively small intervention through small grants and technical cooperation assistance might cause a disproportionably significant impact. The study suggests that donors may already be funding these kinds of ‘high leverage’ initiatives, but that current reporting procedures and increasing interest in large-scale budget support may mean that these activities and the factors that might contribute to their success are not well documented. These factors are likely to include: the country-specific context; the approach of the donor agency to aid effectiveness; how it understands change; how it invests in relationships; its openness to a diversity of views; and its preparedness to experiment, take risks and learn to alter its views. All of these can be seen as ways to manage the context within which change happens.

The inherent unpredictability of change in such systems also means that there may be significant value in an organisation increasing its ‘agility’. In systems characterised by ‘surprise and discontinuity … organisations need to rapidly adapt to unexpected conditions … they have to improvise’ 27. Thinking back to concept 5 and the implications for planning, this does not – as some claim – imply that strategy becomes irrelevant: ‘the idea of strategising for the future is fundamentally based on the unpredictability of the future, of which some aspects … can be foreseen’. To put it another way, working with chaos means ‘it is not about being strategic or opportunistic; it is about being strategically opportunistic’ (John Young, personal communication). It has been suggested that ‘the adoption of minimally structured organisational forms are a necessary condition for strategic improvisation’ 28.

These factors also resonate with suggestions made for dealing with continuous transformation and change. Specifically, some see the potential turbulence of chaotic systems as emphasising the high importance of making continuous learning an inherent part of organisations and policy. To continue the quote from Jake Chapman cited … [above]:

Our society and all of its institutions are in continuing processes of transformation … we must learn to understand, guide, influence and manage these transformations. We must make the capacity for undertaking them integral to ourselves and our institutions. We must, in other words, become adept at “learning”. This “learning” should not be seen as a one-off event, or a case of acquiring new knowledge or skills, rather it involves ongoing practice and reflection on one’s own experience. Since knowledge of “best practice” cannot be easily imported from elsewhere, all organisations must involve themselves in learning as a “continuous, on-the-job process” 29

This should be done through a commitment to ongoing reflection and adaptation of aid programmes. Since the context in which a programme is operating is continuously changing, and it is not possible to plan for all eventualities, a successful programme is one that assesses and adapts to changing situations in an intelligent way based on thoughtful reflection. This means that the programme needs to be engaged in ongoing reflection and learning so as to remain relevant and appropriate. This shift towards ongoing processes of learning has some knock-on implications. It has been suggested that this entails a move in attitude away from ‘knowing best’ (‘if one already knows the answer or knows best then there is no need to learn anything’)30, realising that there are ‘no final answers’ and we must approach problems with the mindset of ‘enquiry and not certitude’ 31. This could shift focus of policy from ‘specifying targets to be met’ towards ongoing work ‘based on learning what works, and towards improving overall system performance, as judged by the end-users of the system’ 32. But a chaotic system not only suggests that lessons themselves are permanently provisional33, but also calls for an approach to learning and decision making that is tailored to the specific situation.

Although it is tempting to suggest methodologies that enable this kind of thinking to be implemented, such as soft systems methodology, or outcome mapping, or most significant change, in reality this reinforces the notion that tools are useful but no single tool should be expected to provide all of the guidance needed for decision making. Similarly, no single tool should be expected to provide the most appropriate means by which to arrive at guidance. The notion of strange attractors and chaos goes further and suggests that no single mindset should be seen as the appropriate to all settings.

Researchers from IBM [Kurtz and Snowden]34 have done interesting empirical studies which relate to different kinds of organisational systems, with careful attention paid to those which feature chaotic dynamics. Kurtz and Snowden characterise certain decision-making and learning approaches as most appropriate to different kinds of systems. For example, approaches that focus on sensing incoming data, categorising it and responding in accordance with established practice are most appropriate in systems that are ordered and known, for example, when undertaking business process re-engineering, in which cause and effect relationships are seen as linear and understood. Examples of such approaches are single-point forecasting, field manuals and operational procedures.

By contrast, approaches that focus on sensing data, analysing it and then responding in accordance with expert interpretation and advice are most useful in complicated systems, for example, organisational learning initiatives, or strategic futures planning efforts, where there may be stable cause-and-effect relationships, and in which everything can be understood, given sufficient resources and time. Examples are experimentation, expert opinion, fact finding and scenario planning. While structured techniques are desirable, underlying assumptions must also be open to examination and challenge.

Finally, approaches that focus on sensing patterns of change and understanding multiple perspectives, and working to strengthen wanted patterns and weakening the unwanted are most appropriate in complex systems characterised by multiple feedback processes and interaction among many agents, emergent properties, nonlinear relationships and limited predictability. In such systems, many examples of which have been covered already, the application of structured methods will frequently confront new and different patterns for which they are not prepared, and approaches need to be tailored to the nature of the problem.

Sanders, I. (1998). Strategic Thinking and the New Science: Planning in the Midst of Chaos, Complexity, and Change, Columbus OH: The Free Press. ↩

If any part of the strange attractor were magnified, it would reveal a multi-layered sub-structure in which the same patterns are repeated. Complexity plays out in identical ways at different levels of a system. The development of fractal geometry by the IBM researcher Benoit Mandelbrot has helped to further understanding of chaos, to the extent that the term ‘fractal’ is now widely used to describe the computer-generated images created when mapping strange attractors (Gleick, J. (1987). Chaos: Making a New Science, New York: Viking.) ↩

]]>https://realkm.com/2019/03/18/exploring-the-science-of-complexity-series-part-16-concept-7-strange-attractors-and-the-edge-of-chaos/feed/0Balancing organisational remembering and forgettinghttps://realkm.com/2019/03/15/balancing-organisational-remembering-and-forgetting/
https://realkm.com/2019/03/15/balancing-organisational-remembering-and-forgetting/#respondThu, 14 Mar 2019 22:30:31 +0000https://realkm.com/?p=14674To date, knowledge has largely been seen as something positive that can be beneficially managed. However, in a recent article we reported on a paper in which authors Susanne Durst and Malgorzata Zieba argue that organisations also need to consider knowledge risks. This is because of the emerging number of these risks and the growing complexity of organisational environments. Following on from the Durst and Zieba paper, we’ve also discussed another paper that reviews the literature in regard to one of the identified knowledge risks – knowledge hoarding.

A new conference paper1 focuses on two more of the knowledge risks – forgetting and unlearning. Durst and Zieba describe these as:

Forgetting – can be both accidental (due to bad memory) or intentional (trying to avoid bad habits)

Unlearning – a type of deliberate forgetting which involves a conscious process of giving up and abandoning knowledge, values, and/or practices which are deemed to have become outdated in an organisation.

The author of the new conference paper, Ana Aleksić Mirić, alerts that while much of the existing research in regard to organisational knowledge is focused on organisational learning per se, real-life practice teaches us that companies don’t just learn, they also forget. She advises that an easy way to understand the process of organisational forgetting is to compare it to what happens with individuals. Either intentionally or unintentionally, people forget. The things that are forgotten are usually regarded as less important or unimportant, but eventually, even very important things won’t be remembered.

Organisational forgetting can be either unintentional or intentional.

Unintentional forgetting is where important knowledge is lost from the memory of the organisation, and so not available when it is potentially needed in the future. For example, the forgetting of an organisational memory of how to effectively deal with a particular problem that is occasionally experienced. The loss of this organisational memory means that when the problem does reoccur it will potentially create a serious crisis that could otherwise have been quickly addressed. The consequences of this can be extremely serious, such as a loss of client or consumer confidence and the consumption of a large amount of resources to reinvent a solution.

Intentional forgetting involves the deliberate unlearning of old patterns of behaviour and previously acquired knowledge, so that new knowledge and skills can be acquired and put into practice. This can be necessary when an organisation alters the way in which it does business, for example when offering new products or services or entering different markets, or when the external environment shifts. The organisational response needs to involve both behavioral and cognitive changes.

Mirić argues that this organisational forgetting is not the opposite of organisational learning, because the deliberate forgetting (unlearning) aids learning:

Having explained learning as a process, I argue that organizational forgetting is not on the opposite side of organizational learning. If intentionally led and carefully managed, organizational forgetting can be one of [the] organizational processes that contributes to organizational health and enhances further learning; through forgetting, organizations clean unnecessary and outdated ways of thinking and behaviour, and makes room for a new relevant knowledge to be planted.

She concludes by stating that intentional organisational forgetting can be readily facilitated within the existing knowledge management functions and activities of the organisation. This might be true, but as Mirić herself has warned, we don’t yet have enough evidence to confidently draw that conclusion. Very little of the existing research addresses organisational forgetting – more research is needed.

]]>https://realkm.com/2019/03/15/balancing-organisational-remembering-and-forgetting/feed/0Three initiatives that are helping to address the global knowledge imbalancehttps://realkm.com/2019/03/14/three-initiatives-that-are-helping-to-address-the-global-knowledge-imbalance/
https://realkm.com/2019/03/14/three-initiatives-that-are-helping-to-address-the-global-knowledge-imbalance/#respondThu, 14 Mar 2019 11:43:06 +0000https://realkm.com/?p=14661This article is part two ongoing series of articles: cultural awareness in KM and KM in international development.

As I revealed in a previous RealKM Magazine article, if the world is mapped according to how many scientific research papers each country produces, it takes on the bizarre, uneven appearance above. Note the bloated size of the United States, the United Kingdom, and Europe compared to South America and in particular Africa.

This is a serious issue, because it means that what many would regard as globally universal behaviours and processes can’t actually be considered as such on the basis of the available evidence. Concerningly, practices and approaches developed from research in what have come to be known as WEIRD (Western, educated, industrialized, rich and democratic) contexts can be culturally incompatible or inappropriate in other settings.

As awareness of the global knowledge imbalance grows, initiatives aimed at helping to address it are starting to emerge. Three notable examples are the Citing Africa podcast series, Wuṉḏaŋarr Yolŋu Gurruṯu (Strong Yolŋu Families) resource, and 2019 International Year of Indigenous Languages.

Citing Africa

The London School of Economics and Political Science (LSE) recently launched the Citing Africa podcast project, which is:

investigating the decline of Africa-based contributions in top international academic journals

providing practical guidance to young scholars seeking to publish their own work

taking a critical look at the wider context of knowledge production about the African continent.

The first podcast in the series of nine episodes was made available in early March, and the remaining episodes will be added in the coming weeks.

Wuṉḏaŋarr Yolŋu Gurruṯu

Australia’s ABC Newsreports on the publication of a “Breakthrough resource to teach whitefellas about reality of life in Arnhem Land.”

The ARDS Aboriginal Corporation has produced the new Wuṉḏaŋarr Yolŋu Gurruṯu (Strong Yolŋu Families) booklet to guide non-indigenous practitioners in their work with Yolŋu people affected by family violence. It provides advice on working in ways that are culturally safe and socially accountable, alerting that:

breaking cycles of violence and finding restorative pathways requires a strengths-based approach centred on gurruṯu (kinship). A strengths-based approach acknowledges the cultural mismatch between Aboriginal and Torres Strait Islander cultures and dominant Western systems, and seeks ways to work with and build on the strengths of Indigenous cultures.

2019 International Year of Indigenous Languages

Of the almost 7,000 existing languages, the majority have been created and are spoken by indigenous peoples who represent the greater part of the world’s cultural diversity.

Yet many of these languages are disappearing at an alarming rate, as the communities speaking them are confronted with assimilation, enforced relocation, educational disadvantage, poverty, illiteracy, migration and other forms of discrimination and human rights violations.

Given the complex systems of knowledge and culture developed and accumulated by these local languages over thousands of year, their disappearance would amount to losing a kind of cultural treasure. It would deprive us of the rich diversity they add to our world and the ecological, economic and sociocultural contribution they make.

]]>https://realkm.com/2019/03/14/three-initiatives-that-are-helping-to-address-the-global-knowledge-imbalance/feed/0How doctors can use AI to have better conversations with patientshttps://realkm.com/2019/03/13/how-doctors-can-use-ai-to-have-better-conversations-with-patients/
https://realkm.com/2019/03/13/how-doctors-can-use-ai-to-have-better-conversations-with-patients/#respondWed, 13 Mar 2019 12:00:31 +0000https://realkm.com/?p=14655Originally posted on The Horizons Tracker.

Rarely are conversations as important as those between a doctor and their patient. Being able to communicate often complex and distressing information in a clear and understandable manner is crucial. A recent paper1 from researchers at Trinity College Dublin, the University of Edinburgh and The Dartmouth Institute for Health Policy and Clinical Practice explores the possibility of using AI to improve the communication between doctor and patient.

“Many clinicians’ communications skills aren’t formerly assessed–either during school or in early practice. At the same time, there is a lot of evidence that clinicians often struggle when communicating with their patients. It’s hard to improve on something when you’re not being given any feedback and don’t know how you’re doing,” the authors say.

The researchers suggest that AI can help to improve doctors communication skills by giving them detailed and personalized assessments of their communication abilities. What’s more, it can do so at a much lower price than current methods. The authors believe the benefit can broadly be boiled down into three core areas:

Analysis of words and phrases – by automating the analysis of words, the researchers believe that technology can help the doctor understand whether the patient understood them, and indeed whether they understood the patient. Eventually, they believe this analysis can be performed in real-time, with prompts given to doctors on how they can improve the conversation, and potentially even offer new treatments that the doctor themselves had not thought of.

Turn-taking analysis – by looking at the amount of time each party spends talking, the researchers believe AI can help to assess the structure of the conversation. For instance, is the doctor allowing sufficient time for the patient to absorb the information and ask any questions they may have? The best conversations should be two-way discussions rather than a monologue. As before, eventually they believe the technology can provide real-time analysis and intervene when things are going badly awry.

Tone and style of interactions – this form of analysis is already common in the aviation sector, with pilots communication assessed for its vocal pitch and energy. The authors believe that adopting a similar approach in healthcare could help to detect high-risk situations that place the doctor under extreme stress. It could also provide an insight into the patients’ mental wellbeing.

Whilst healthcare poses unique challenges in terms of the complexity of the language used, the researchers believe that AI can eventually prove to be invaluable in analyzing the effectiveness of communication between doctor and patient.

“Five years ago, the idea of using AI to analyze medical communication wouldn’t have been on anyone’s radar,” they conclude. “As the technology advances, it will be interesting to see whether healthcare systems can employ it effectively and whether providers will be open to using it as a tool for improving their communication skills.”

]]>https://realkm.com/2019/03/13/how-doctors-can-use-ai-to-have-better-conversations-with-patients/feed/0Exploring the science of complexity series (part 15): Concept 6 – Phase space and attractorshttps://realkm.com/2019/03/13/exploring-the-science-of-complexity-series-part-15-concept-6-phase-space-and-attractors/
https://realkm.com/2019/03/13/exploring-the-science-of-complexity-series-part-15-concept-6-phase-space-and-attractors/#respondWed, 13 Mar 2019 10:36:44 +0000https://realkm.com/?p=14637This article is part 15 of a series of articles featuring the ODI Working Paper Exploring the science of complexity: Ideas and implications for development and humanitarian efforts.

Complexity and systems – Concepts 4, 5, 6, and 7

The next four concepts relate to different aspects of how complex systems – those characterised by Concepts 1-3 – change over time. The causal relationships that play out within complex systems are explained using the concept of nonlinearity (Concept 4) and the sensitivity of complex systems to their starting conditions is highlighted (Concept 5). The overall shape of the system and its future possibilities are described using the idea of phase space (Concept 6). The patterns underlying seeming chaos within complex systems are explained (Concept 7).

Concept 6 – Phase space and attractors

Outline of the concept

The dimensions of any system can be mapped using a concept called phase space, also described as the ‘space of the possible’ 12. For any system, the ‘space of the possible’ is developed by identifying all the dimensions that are relevant to understanding the system, then determining the possible values that these dimensions can take3. This ‘space of the possible’ is then represented in either graphical or tabular form. In natural sciences, the prevalence of time-series data means that the phase space can be represented as a graphical map of all of the relevant dimensions and their values. In social scientific thinking, tables of data can be used to apply the same principles. The phase space of a system is literally the set of all the possible states – or phases – that the system can occupy.

Phase space is particularly useful as a way to describe complex systems because it does not seek to establish known relationships between selected variables, but instead attempts to shed light on the overall shape of the system by looking at the patterns apparent when looking across all of the key dimensions. This resonates with a key point raised in Concept 1 – more may be learned about complex systems by trying to understand the important patterns of interaction and association across different elements and dimensions of such systems4. Phase space can be used to enable this kind of learning. By creating such a map of a system, it is possible to characterise how that system changes over time and the constraints that exist to change in the system5.

Detailed explanation

The dimensions of a complex system mutually influence each other, leading to an intricate intertwining6 of these relationships and system behaviours to degrees of nonlinearity and unpredictability. Because of the challenges involved in analysing such systems, scientists studying complex systems have made use of a mathematical tool called phase space, which allows data relating to the dimensions of a system to be mapped rather than solved7. Put simply, phase space is a visual way of representing information about the dimensions of a system. Rather than a graph, which attempts to show the relationships between specific chosen dimensions, phase space maps the possible values of each dimension of the system (akin to drawing the axes of a graph). This is the space within which a complex system displays its behaviour.

Byrne gives the example of a city as a complex system8. He describes how the cities are complex problems in that they present situations where a range of variables are interacting simultaneously and in interconnected ways. He cites the specific example of Leicester, a city in the UK that grew from a small market town of 2,000 people in the 11th century to a city of 280,000 in 2001. Using the Census data from 2001, he shows that Leicester could be seen as a complex urban system made up of the following variables:

Total population of the area

Ethnic composition of the population

Distribution of the population between urban core and suburban / exurban periphery

Proportion of population aged 16-74 which is economically active

Total employed population

Distribution of the working population by sector of employment

Distribution of the working population by kind of economic activity

Distribution of the working population by gender

Total number of households

Tenure of households

These variables together represent the individual dimensions of the multi-dimensional phase space of the Leicester urban area. With year on year data over a number of years, this table can represent the phase space of the system. Figure 1 provides a picture of Leicester using two time points.

We will return to some of Byrnes’s work later, but it is important now to look at the idea of phase space in rather simpler systems in order to illustrate some key principles. The most commonly used simple system is that of a pendulum swinging through a small angle without friction (the top image in Figure 2 below).

Mapping these data points over time shows that the system moves in a circle or ellipse through this phase space. This loop is called the system’s trajectory in phase space. The trajectory provides a metaphorical map of how the values of the system dimensions change over time. The dimensions of the system at any given point can be represented by a single point which will always be on the trajectory.

In the real world, if friction is taken into account in a pendulum system, the assumption would be that the pendulum would eventually run out of energy and come to a standstill. This is described below.

The above examples illustrate how phase space enables an understanding of the evolution of the systems: as the system changes, the point marking its location in phase space also moves. The diagrams show how the variables of the system change and how they are interconnected: the velocity is highest when the pendulum is in the centre of its swing, and moves to zero at either end of swing. A pendulum under friction swings smaller and smaller, until it eventually comes to a stop. The graph of position versus time shown in Figure 2 should help clarify that phase space allows a complementary understanding of the dynamics of a given system.

Phase space, in the sense described above, was developed in the early 20th century, but it is with the advent of computing and new levels of number-crunching power that it has come into its own. The same basic technique described above can be used for systems that are far more complex and have many more dimensions. Computers have enabled tracking of systems with many dimensions and have been used to identify the patterns underlying seemingly complex behaviour.

The ways in which the dimensions evolve and interact leads to different system behaviours in phase space10. The trajectory of a complex system through its phase space may be totally random, such that at any point in time the system could be anywhere in its phase space. Conversely, the system may be limited to a particular part of the phase space – as in the simple example of the pendulum shown above. When observed over time, the points can start to form recognisable patterns. These patterns are known as attractors, and they embody the long-term qualitative behaviour of a
system11.

The first pendulum described above keeps swinging, and so it is said to have a periodic attractor – it moves through its phase space periodically, in repeated ways. The second diagram describes a pendulum that eventually comes to a rest, and so is said to be a point attractor.

Imagine another simple system of a pencil being put down on its end (see Figure 4). There are two possibilities for how this system could end up – or two attractors. The pencil could end up horizontal, or vertical. In this example, A is possible attractor, but B is the dominant attractor. The lower diagram shows phase space in a different way – in relation to the potential energy of the system.

Figure 4. The two attractors of a pencil being placed on a table (source: Newell 200312).

Work on strengthening higher education systems in former Soviet bloc countries has highlighted that the concept of phase space is a powerful one in tracking their evolution and therefore informing policy decisions. The higher education system phase space is made up of a wide range of dimensions: number of institutions, private or state institutions, students, staff, etc.13. The phase space is developed by tracing how these dimensions change over time14. Each dimension can have a certain, limited set of values – for example, if a university system can educate maximum number of x students, the dimension ‘number of students’ can have values ranging from 0 to x, giving rise to a range of different states15. Using this approach can reveal that different dimensions are alternatively constant, stable, evolving or unpredictable.

A higher education system would typically move slowly through its phase space by means of changes in various dimensions – for example, changes in the number of students, in the way that degree examinations are administered, in the results in examinations, or in the number of institutions. The figures for every year may be somewhat different, but not to a degree that will change dramatically the character of the university system.

If, however, new government regulations set a fee for university attendance, which has been free till this moment, a dramatic change will occur. The university may react in too many different ways to the new regulations – it may introduce scholarships, reduce the student places, make some of its staff redundant or cut their salaries or, if there are not enough students enrolling, even close down. This process of sudden movement in the phase space, which is not uncommon for complex systems, is called a phase transition, or ‘bifurcation’, which will be looked at in more detail in Concept 7.

In general, complexity scientists distinguish between conservative systems, which essentially do not have the potential to transform the general shape of their attractor, and dissipative systems, where the form of the attractor may change. The concept of equilibrium is useful here: there might be systems at equilibrium, which do not change their position in phase space; there may be systems close to equilibrium, such that they move back to their original position if disturbed; and there may be systems far from equilibrium, such that radical transformation of the trajectory through phase space is possible. Complex and chaotic systems are described by particular kinds of attractors, which will be described in more detail in Concept 7.

With sufficient data, the phase diagram can be used as a representation (albeit an approximate one) of all the states the system can possibly occupy over time. This has given rise to the term ‘space of the possible’ 16. Over time, the behaviour of a complex system can be mapped by observing the movements of these points within the phase space1718. Complex social, economic or political systems can be seen to develop within certain economic, ecological, political and socio-cultural constraints, which can be seen as maintaining the boundaries of the phase space of the system19. Though such systems may be unpredictable in their behaviour, there may be patterns describing the boundaries, or the possibility space, of the system’s behaviour. The shape of phase space can therefore be used to uncover and examine aspects of the system that might not be otherwise obvious.

Example: Phase space and understanding socioeconomic exclusion

In social sciences, a tabular form of representing data may be used to show phase space of the system20. This can be particularly useful when analysing qualitative data. For example, data below come from a household survey in Cleveland undertaken with over 1,500 respondents. The survey contained a great deal of information about household structure and employment relations. Byrne uses these data to construct a six-dimensional phase space of the households, as shown below.

Figure 5. Tabular representation of socio-economic system with six dimensions (source: Byrne 199821).

If each household could have one value for each of these dimensions, there are 900 different types of households. By looking at the typical combinations of values, it is possible to draw conclusions about the phase space of households, and how this changed over time. Given the data available over time, Byrne was able to use this to analyse how the form of the phase space of excluded households had changed over time.

While some of the potential combinations were not possible because of how data were selected, it was clear that the overall phase space had relatively stable configurations – for example, single parent families were worse off, and their numbers remained reasonably steady over time. By looking at the patterns with many entries, and with few, it is possible to see what phase space conditions are possible in Cleveland at a given point in time. In the example given, exploration of the phase space showed some interesting concentrations. For example, the work poor and young-headed households were very likely to be social housing tenants. The work rich young were more likely to be owner occupiers. Because the Cleveland Social Survey was conducted on an annual basis from 1977 to 1995, it was possible to construct the phase space specified by the six variables for a series of points over time, thereby examining changes in this phase space as a socioeconomic system. During this period, there was a major shift in the attractors for the system. In the late 1970s, social housing was not associated with work poverty. Most very deprived households were female single parent-headed, and there were relatively few of them. By the 1990s, most work poor households were double headed, and the absolute numbers of such households had grown dramatically. They predominated among the social housing tenants in Cleveland. The category of ‘no social class assignable’ became much more significant, because the growth in unemployment meant that there was no employment basis for the assignment of class. In the language of social exclusion, in the late 1970s the attractor of excluded households contained a relative minority of child-containing households in which the households were primarily female headed. By the 1990s, the attractor was much larger in terms of child-containing households, and contained more two-parent households than single-parent households. The phase space analysis shows that exclusion had become de-gendered and had massively expanded.

Implication: Build understanding of the ‘space of the possible’

The concept of phase space suggests complex systems such as those faced by aid organisations are not best understood by simply ‘carving out’ a number of the dimensions and analysing these as a subset. Similarly, it is not ideal to try to understand a variety of causes that have led the dimensions of a system to be the way they are, then moving on to examine each dimension and its cause in isolation. Attempts to understand the system should first identify the key dimensions and track changes in them over time, using this to develop a holistic picture of how the system changes and evolves.

For example, the phase space of an international agency (following Romenska22) would contain dimensions describing the extent to which it is state or privately funded, parameters showing the numbers of staff and offices worldwide, dimensions for the number of programmes and projects and for the number of beneficiaries – the list can be endless. By tracking the changes of all these dimensions over time – the phase space – it is possible to characterise how the organisational system changes in response to internal and external interactions. Certain dimensions may be constant (the number of offices), stable (yearly income from government donors), evolving (the information infrastructure of the organisation), or unpredictable (staff composition). The quest for organisational change will require those involved to appreciate the multiple dimensions of the organisation, which can be influenced and which cannot, which are constrained and by what, and how these issues play out between the different levels of the organisation. Importantly, if certain dimensions cannot be changed, it may be because they are not being addressed at the appropriate level of the organisational system. For example, it may be hard to influence and improve learning processes across international offices, but much easier at the level of teams.

This aspect of phase space highlights an important issue that has been raised recently with regards to changes in humanitarian organisations. As part of a broader look at realistic expectations for change in the humanitarian sector23, it has been suggested that the next five years will see mixed progress in ‘outer realm’ issues of politics, and deeper changes in the ‘inner realm’ of organisational efficiency and effectiveness. If the humanitarian system is seen to develop within broader economic, ecological, political and socio-cultural constraints, then this statement should be qualified carefully with the understanding that the ‘outer realm’ may maintain certain aspects of the system such that the ‘inner realm’ issues are effectively constrained by the lack of progress in the outer realm. A clearer understanding of how the ‘outer’ and ‘inner’ dynamically affect each other would be a useful start.

Fully utilising the concept of phase space calls for aid organisations at least to make an attempt to understand the full range of different dimensions of the systems with which they are dealing, the values these dimensions might take over time, and the implications of this for how the system changes and evolves. Ultimately, there should be effort to contextualise the projects or programmes of an agency within these patterns of system behaviours. Enabling such an understanding shapes how aid projects and programmes are conceived, planned and executed.

Next part (part 16): Concept 7 – Strange attractors and the ‘edge of chaos’.

Phase space is often used interchangeably with the phrase ‘state space’. ↩

Romenska, S. (2006). ‘Innovation in Higher Education Systems in the Post-socialist Countries in Central and Eastern Europe, 1999-2005: Possibilities for Exploration through a Complexity Theory Framework’, Research in Comparative & International Education 1(2). ↩

Haynes, P. (2003). Managing Complexity in the Public Services, Berkshire: Open University Press. ↩

Newell, D (2003). ‘Concepts in the study of complexity and their possible relation to chiropractic healthcare’ in Clinical Chiropractic (2003) 6, 15-33. ↩

Romenska, S. (2006). ‘Innovation in Higher Education Systems in the Post-socialist Countries in Central and Eastern Europe, 1999-2005: Possibilities for Exploration through a Complexity Theory Framework’, Research in Comparative & International Education 1(2). ↩

Romenska, S. (2006). ‘Innovation in Higher Education Systems in the Post-socialist Countries in Central and Eastern Europe, 1999-2005: Possibilities for Exploration through a Complexity Theory Framework’, Research in Comparative & International Education 1(2). ↩

Romenska, S. (2006). ‘Innovation in Higher Education Systems in the Post-socialist Countries in Central and Eastern Europe, 1999-2005: Possibilities for Exploration through a Complexity Theory Framework’, Research in Comparative & International Education 1(2). ↩

]]>https://realkm.com/2019/03/13/exploring-the-science-of-complexity-series-part-15-concept-6-phase-space-and-attractors/feed/0Inverting investment modelling to add rigour to benefits managementhttps://realkm.com/2019/03/07/inverting-investment-modelling-to-add-rigour-to-benefits-management/
https://realkm.com/2019/03/07/inverting-investment-modelling-to-add-rigour-to-benefits-management/#respondThu, 07 Mar 2019 10:05:40 +0000https://realkm.com/?p=14565One of the classic problems facing corporate capabilities such as knowledge management (KM), information management, human resource (HR) management, and training is that it can be difficult to justify their expense. In a 2010 article, Patrick Lambe questioned whether we should even be trying to evaluate return on investment (ROI) for these functions, writing:

[When corporate capabilities are] not budgeted at the project level, discussions about ROI have traditionally had little relevance. It would be like seeking the ROI on groceries in a personal budget or of stationery supplies in a business. Both are simply costs of doing business.

The problem is that unlike groceries or stationery, the absence of these corporate functions are not universally recognised as essential or non-discretionary costs, particularly when any attempt is made to allocate funding above the fundamental bare minimums of the function (such as having a place to store documents, and an ability to get employees paid).

A common approach for business cases for KM and HR projects is to develop a classic return on investment (ROI) calculation, claiming “X minutes saved per search” or “X% lower turnover” and extrapolating a benefit based on per-hour employee costs. These are used to justify green-lighting of initiatives, often with little attempt made to actually substantiate claims after their completion. In one particularly notorious case, the Australian Taxation Office (ATO) claimed $130m in savings over 3 years as part of its Reinventing the ATO initiatives – yet the Australian National Audit Office (ANAO) found that just $135,000 of those savings could be verified!

reinvested benefit — larger productivity benefits that can be reinvested (such as a full-time equivalent position).

Sometimes known as harvestable savings, reinvested benefits are realised once teams commit to staff redeployment or termination at the conclusion of an initiatives. Most teams in public services agencies are understandably reluctant to commit to these savings when future workload can be unpredictable. Notional savings, on the other hand, are meant to “free up” staff to do other things but the hypothetical “better use of time” is rarely specified.

This is where the investment calculation tool known as the internal rate of return (IRR) presents an interesting alternative to traditional ROI. IRR is a method for picking between alternative investments that takes into account total cost to implement, and the net cash received per period as a result of the project. This approach mirrors the notional benefits claimed to be unlocked through non-harvestable initiatives, but in terms of costs incurred rather than cash received.

In a McKinsey article critiquing the use of IRR, the point is made that any claimed rate of benefits can only be realised if the resources freed can be redeployed in an equally productive way elsewhere. Put simply: a notional saving of 2,000,000 minutes in employee time per month is only useful if those minutes aren’t then spent taking an extra coffee break each day.

In truth, identifying notional savings is only doing half the job. Conscious reinvestment of time “savings” towards measurable outcomes such as higher case throughput, higher customer satisfaction, lower rate of legal cases, or or some other outcome that the organisation values – even if this is different for each line area – is the only way to ensure that value is actually being realised from productivity initiatives.

Just as identifying an “increase in the potential revenue ceiling” is unlikely to satisfy investors in an annual report, reporting that there is now a “notional saving” from employees freeing up time is unlikely to prove that things are getting better. It is essential that organisations identify measurable avenues for employee time reinvestment before commiting to projects delivering such “notional savings”.

]]>https://realkm.com/2019/03/07/inverting-investment-modelling-to-add-rigour-to-benefits-management/feed/0Nudge initiative creates confusion and undermines trusthttps://realkm.com/2019/03/07/nudge-initiative-creates-confusion-and-undermines-trust/
https://realkm.com/2019/03/07/nudge-initiative-creates-confusion-and-undermines-trust/#respondThu, 07 Mar 2019 01:19:44 +0000https://realkm.com/?p=14624A recent article from ABC News Australia reports on criticisms of a behavioural insights initiative by the government of the Australian Capital Territory (ACT). The ACT includes Australia’s capital city Canberra, which is the seat of the government of Australia, however the democratically-elected ACT Government is responsible for governing just the ACT.

The behavioural insights approach, also known as ‘nudging’, has been made famous by the UK’s Behavioural Insights Team, which is also known as the ‘nudge unit’. Wikipedia advises that nudge theory rose to prominence with the publication of Richard Thaler and Cass Sunstein’s 2008 bookNudge: Improving Decisions About Health, Wealth, and Happiness:

Nudge is a concept in behavioral science, political theory and behavioral economics which proposes positive reinforcement and indirect suggestions as ways to influence the behavior and decision making of groups or individuals.

The ABC News article states that the ACT Government engaged a researcher from the Australian National University (ANU), which is also located in Canberra, to conduct ‘behavioural insights’ trials aimed at nudging ratepayers into settling their accounts more quickly. The trials involved the redesign of billing forms.

However, the form redesign resulted in confusion because it appeared that ratepayers had to pay the entire annual amount in one payment, rather than being able to pay in quarterly instalments as had previously been the case. This led to derision of the form changes, with ABC News quoting ratepayers as saying that the redesign was a “mean and tricky way to get people to pay their rates notices in full.”

In a previous RealKM Magazine article, I reviewed a range of research criticising nudge theory. The criticisms I put forward include that nudge theory ignores the full range of determinants of behaviour, and that nudging is paternalistic, manipulative, and sometimes deceitful.

The ACT Government’s behavioural insights trial demonstrates the validity of these criticisms, with ABC News quoting the ACT Government opposition leader as saying that:

I think it was misleading, and the fact that they’ve had to engage these sort of tricks to get people to pay their rates notice I think demonstrates the hardship that people in Canberra are feeling due to cost-of-living issues.

There will be a range of reasons why some ratepayers are late with their payments. While the ACT Government’s nudge approach might address some of these reasons, for example people who just don’t bother to pay, it ignores other determinants of the late payment behaviour, for example financial hardship. For those in financial difficulty, the nudge approach will just add further stress while solving absolutely nothing.

Further, ABC News reports that the behavioural insights trial was only revealed through a freedom of information request. This apparent secrecy highlights the paternalistic, manipulative, and arguably deceitful nature of the ACT Government’s nudge approach.

I find it quite extraordinary that an Australian government would consider such an approach when the 2019 Edelman Trust Barometer warns of widespread distrust in governments, and the Democracy 2025 project alerts that:

Despite 25 years of economic growth – which traditionally means increased satisfaction – Australians have grown more distrustful of politicians, sceptical about democratic institutions and disillusioned with democratic processes.

It’s time for everyone to put away their copies of Nudge: Improving Decisions About Health, Wealth, and Happiness, and instead engage with initiatives like Democracy 2025 which aims to re-engage an increasingly disenfranchised and dissatisfied community.

Header image: The 1959 Ethos sculpture is located outside the public entrance to the ACT Government Legislative Assembly Building. Ethos was conceived as “the spirit of the community”, but sadly the spirit of the community is apparently being nudged out of ACT Government decision-making. (Source: Adapted from Wikimedia Commons, CC BY-SA 4.0).